Technical session talks from ICRA 2012

TechTalks from event: Technical session talks from ICRA 2012

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Hand Modeling and Control

Redundant tendon-driven systems such as the human hand or the ACT robotic hand are high-dimensional and nonlinear systems that make traditional control strategies ineffective. The synergy hypothesis from neuroscience suggests that employing dimensionality reduction techniques can simplify the system without a major loss in function. We define a dimensionality reduction framework consisting of separate observation and activation synergies, a first-order model, and an optimal controller. The framework is implemented for two example tasks: adaptive control of thumb posture and hybrid position/force control to enable dynamic handwriting.

For the purpose of ergonomic human-machine interaction and geometrical design of hand held haptic devices, a kinematic model that represents the functional anatomy of different human hands is desired. It is the goal of this paper to present a kinematic hand model that is based on human physiology and that is easily adaptable to represent various real human hand sizes. This is achieved by exploiting body proportions to derive finger segment lengths from the hand length. A partial hand model validation, involving index- and middle finger validation using a group of subjects, indicates that the use of body proportions offers a good estimate of finger length from a given hand length. Model estimated fingertip positions over a motion trajectory remain within reasonable limits when compared with experimental data for this subject group. The model is promising for usage in practical situations since only hand length, which is easy to measure or to obtain from literature, is required as an input. Phalange lengths, which are sparsely available from literature and difficult to measure, are generated by the model.

Musculoskeletal models are effective tools for understanding living systems. To ensure proper model function, they must be checked against the literature or specimens. Existing checking methods require cadaver experimentation, highly knowledgeable medical personnel, and/or significant time. In this paper, we propose a quick and efficient method, called functional consistency checking, for use when these resources are not available. This method uses the literature to define a set of mathematical constraints, custom inverse dynamics software to interact with the model and its Jacobian in realtime and then evaluates the models consistency with these constraints. The method's usefulness will be demonstrated by constructing a human hand prototype, performing functional consistency checking, and then comparing the original to the output using data from a pianist motion capture.

This paper presents a novel approach to deal with uncertainty in grasping. The basic idea is to initiate a caging manipulation state and then shrink fingers into immobilization to perform a practical grasping. Thanks to flexibility from caging, this procedure is intrinsically safe and gains tolerance towards uncertainty. Besides, we demonstrate that the minimum caging is immobilization and consequently propose using three or four fingers to manipulate planar convex objects in a grasping-by-caging way. Experimental results with physical simulation show the robustness and efficacy of our approach. We expect its leading benefits in saving finger number, conquering low-friction materials and especially, dealing with pose/shape uncertainty.

Caging is a method to make an object inescapable from a closed region by rigid bodies. Position-controlled robot hands can capture an object and manipulate it via caging without force sensing or force control. However, the object in caging is freely movable in the closed region, which may not be allowed in some applications. In such cases, grasping is required. In this paper, we propose a new simple approach to grasping by position-controlled robot hands with the advantage of caging: caging-based grasping by a robot hand with rigid and soft parts. In caging-based grasping, we cage an object with the rigid parts of the hand, and construct a complete grasp with the soft parts. We formulate the caging-based grasping, and derive concrete conditions for caging-based grasping in planar and spatial cases, and show some experimental results.